Research Scientist,Meta Recommendation System Core modeling

Description

The Meta Recommendation Core Modeling team is a central group within Meta focused on building and advancing the core machine learning models that power recommendations across Meta’s products. Our team (MRS Representation Learning)’s mission is to invest in Cross-Meta (ads+organic) joint modeling technologies and unified tech stack to build omni user representation and foundation models to improve Meta’s recommendation systems and maximize business and user value, which is a critical stepping stone in the Meta Recommendation System vision. The team is building advanced recommendation ML models through redesigning model architectures and developing data learning technology at a state-of-the-art level to support a massive user base with accurate predictions and high queries-per-second (QPS) performance.

Responsibilities

Conduct original research in a recommendation/ranking system Design and execute experiments to develop and validate novel ranking modeling techniques, training objectives, and evaluation methodologies Develop scalable approaches to language model pretraining, fine-tuning, and instruction following that advance the state of the art Analyze model behavior, failure modes, and generalization properties to generate actionable research insights Translate research findings into high-quality publications submitted to top-tier venues such as ACL, EMNLP, NeurIPS, ICML, and ICLR Collaborate with cross-functional partners including engineers, product managers, and data scientists to apply language research to Meta's AI systems and products Contribute to open-source releases and reproducible research artifacts that benefit the broader NLP research community Mentor other researchers and engineers on the team, sharing technical expertise and providing feedback on research direction and execution Identify and drive new research directions aligned with the team's long-term goals, proactively scoping projects and building stakeholder alignment Use AI-augmented workflows to expand research productivity, accelerate experimentation, and explore cross-disciplinary problem spaces

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 1+ years of research or industry experience in natural language processing, computational linguistics, or machine learning applied to language tasks Experience developing and publishing novel approaches in NLP or language modeling at peer-reviewed venues such as ACL, EMNLP, NeurIPS, ICML, or ICLR Experience designing, implementing, and evaluating large-scale language models or related deep learning systems using frameworks such as PyTorch Experience communicating research findings and technical trade-offs in writing to both research and non-research audiences Experience working collaboratively on research projects with cross-functional teams Experience mentoring other researchers and contributing to the growth of a research team or community Track record of open-source contributions or reproducible research artifacts in the NLP or machine learning community Experience translating foundational research into production-scale systems or product applications Research experience in language model alignment, reasoning, grounding, multilingual NLP, or efficient training and inference methods for large language models

Compensation: $154,000/year to $217,000/year + bonus + equity + benefits